Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 16 de 16
Filtrar
1.
Cell ; 186(18): 3945-3967.e26, 2023 08 31.
Artigo em Inglês | MEDLINE | ID: mdl-37582358

RESUMO

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.


Assuntos
Neoplasias , Processamento de Proteína Pós-Traducional , Proteômica , Humanos , Acetilação , Histonas/metabolismo , Neoplasias/genética , Neoplasias/metabolismo , Fosforilação , Proteômica/métodos
2.
Cell ; 186(18): 3921-3944.e25, 2023 08 31.
Artigo em Inglês | MEDLINE | ID: mdl-37582357

RESUMO

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.


Assuntos
Neoplasias , Proteogenômica , Humanos , Neoplasias/genética , Oncogenes , Transformação Celular Neoplásica/genética , Variações do Número de Cópias de DNA
3.
Cell ; 173(2): 355-370.e14, 2018 04 05.
Artigo em Inglês | MEDLINE | ID: mdl-29625052

RESUMO

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.


Assuntos
Células Germinativas/metabolismo , Neoplasias/patologia , Variações do Número de Cópias de DNA , Bases de Dados Genéticas , Deleção de Genes , Frequência do Gene , Predisposição Genética para Doença , Genótipo , Células Germinativas/citologia , Mutação em Linhagem Germinativa , Humanos , Perda de Heterozigosidade/genética , Mutação de Sentido Incorreto , Neoplasias/genética , Polimorfismo de Nucleotídeo Único , Proteínas Proto-Oncogênicas c-met/genética , Proteínas Proto-Oncogênicas c-ret/genética , Proteínas Supressoras de Tumor/genética
4.
Bioinformatics ; 34(24): 4315-4317, 2018 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-30535306

RESUMO

Summary: A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three-dimensional protein structures, putative druggable sites that cluster with known druggable mutations. Availability and implementation: http://dinglab.wustl.edu/depo.


Assuntos
Bases de Dados Factuais , Oncologia , Neoplasias/genética , Medicina de Precisão , Genômica , Humanos , Internet , Ferramenta de Busca
5.
BMC Bioinformatics ; 18(1): 281, 2017 May 26.
Artigo em Inglês | MEDLINE | ID: mdl-28549410

RESUMO

BACKGROUND: Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. RESULTS: In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. CONCLUSIONS: The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.


Assuntos
Neoplasias Encefálicas/patologia , Neoplasias do Colo/patologia , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Neoplasias Encefálicas/diagnóstico , Carcinoma/diagnóstico , Carcinoma/patologia , Neoplasias do Colo/diagnóstico , Humanos , Redes Neurais de Computação , Máquina de Vetores de Suporte
6.
BMC Bioinformatics ; 18(1): 35, 2017 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-28088185

RESUMO

BACKGROUND: With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. RESULTS: We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. CONCLUSION: The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package's source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html .


Assuntos
Variações do Número de Cópias de DNA , Perfilação da Expressão Gênica/métodos , Expressão Gênica , Genoma , Humanos , Software , Transcriptoma
7.
Cancer Cell ; 42(7): 1217-1238.e19, 2024 Jul 08.
Artigo em Inglês | MEDLINE | ID: mdl-38981438

RESUMO

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.


Assuntos
Neoplasias Encefálicas , Glioma , Proteína Tirosina Fosfatase não Receptora Tipo 11 , Transdução de Sinais , Humanos , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Neoplasias Encefálicas/metabolismo , Proteína Tirosina Fosfatase não Receptora Tipo 11/genética , Proteína Tirosina Fosfatase não Receptora Tipo 11/metabolismo , Glioma/genética , Glioma/patologia , Glioma/metabolismo , Mutação , Proteômica/métodos , Processamento de Proteína Pós-Traducional , Regulação Neoplásica da Expressão Gênica , Glioblastoma/genética , Glioblastoma/patologia , Glioblastoma/metabolismo , Fosforilação , Gradação de Tumores , Isocitrato Desidrogenase/genética , Isocitrato Desidrogenase/metabolismo
8.
Bioinform Adv ; 2(1): vbac028, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35603231

RESUMO

Motivation: The use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications. Results: Pollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis. Availability and implementation: Source code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

9.
Nat Genet ; 54(9): 1390-1405, 2022 09.
Artigo em Inglês | MEDLINE | ID: mdl-35995947

RESUMO

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.


Assuntos
Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Carcinoma Ductal Pancreático/metabolismo , Transformação Celular Neoplásica/genética , Humanos , Pâncreas/metabolismo , Neoplasias Pancreáticas/metabolismo , Microambiente Tumoral/genética , Neoplasias Pancreáticas
10.
Nat Commun ; 12(1): 2313, 2021 04 19.
Artigo em Inglês | MEDLINE | ID: mdl-33875650

RESUMO

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.


Assuntos
Biologia Computacional/métodos , Mutação , Neoplasias/genética , Proteômica/métodos , Sítios de Ligação/genética , Análise por Conglomerados , Receptores ErbB/metabolismo , Humanos , Espectrometria de Massas/métodos , Neoplasias/metabolismo , Fosforilação , Proteína Tirosina Fosfatase não Receptora Tipo 12/metabolismo , beta Catenina/metabolismo
11.
Cancer Cell ; 39(4): 509-528.e20, 2021 04 12.
Artigo em Inglês | MEDLINE | ID: mdl-33577785

RESUMO

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.


Assuntos
Neoplasias Encefálicas/metabolismo , Glioblastoma/genética , Glioblastoma/metabolismo , Proteína Tirosina Fosfatase não Receptora Tipo 11/metabolismo , Proteogenômica , Neoplasias Encefálicas/patologia , Biologia Computacional/métodos , Glioblastoma/patologia , Humanos , Metabolômica/métodos , Mutação/genética , Fosfolipase C gama/genética , Fosfolipase C gama/metabolismo , Fosforilação/fisiologia , Proteína Tirosina Fosfatase não Receptora Tipo 11/genética , Proteogenômica/métodos , Proteômica/métodos
13.
Nat Commun ; 11(1): 4748, 2020 09 21.
Artigo em Inglês | MEDLINE | ID: mdl-32958763

RESUMO

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.


Assuntos
Genoma Humano/genética , Mutação , Neoplasias/genética , Composição de Bases , DNA Intergênico , Bases de Dados Genéticas , Exoma/genética , Éxons , Humanos , Estudos Retrospectivos , Sequenciamento do Exoma , Sequenciamento Completo do Genoma
15.
Cell Rep Med ; 1(1)2020 04 21.
Artigo em Inglês | MEDLINE | ID: mdl-32529193

RESUMO

In the absence of a dominant driving mutation other than uniformly present TP53 mutations, deeper understanding of the biology driving ovarian high-grade serous cancer (HGSC) requires analysis at a functional level, including post-translational modifications. Comprehensive proteogenomic and phosphoproteomic characterization of 83 prospectively collected ovarian HGSC and appropriate normal precursor tissue samples (fallopian tube) under strict control of ischemia time reveals pathways that significantly differentiate between HGSC and relevant normal tissues in the context of homologous repair deficiency (HRD) status. In addition to confirming key features of HGSC from previous studies, including a potential survival-associated signature and histone acetylation as a marker of HRD, deep phosphoproteomics provides insights regarding the potential role of proliferation-induced replication stress in promoting the characteristic chromosomal instability of HGSC and suggests potential therapeutic targets for use in precision medicine trials.


Assuntos
Instabilidade Cromossômica/fisiologia , Cistadenocarcinoma Seroso , Replicação do DNA/genética , Neoplasias Ovarianas , Fosfotransferases/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , Pontos de Checagem do Ciclo Celular/genética , Estudos de Coortes , Cistadenocarcinoma Seroso/genética , Cistadenocarcinoma Seroso/metabolismo , Cistadenocarcinoma Seroso/mortalidade , Dano ao DNA , Neoplasias das Tubas Uterinas/genética , Neoplasias das Tubas Uterinas/metabolismo , Neoplasias das Tubas Uterinas/mortalidade , Feminino , Regulação Neoplásica da Expressão Gênica , Humanos , Pessoa de Meia-Idade , Mitose/genética , Neoplasias Ovarianas/genética , Neoplasias Ovarianas/metabolismo , Neoplasias Ovarianas/mortalidade , Fosfotransferases/metabolismo , Proteogenômica , Transcriptoma , Proteína Supressora de Tumor p53/genética
16.
Cell Syst ; 9(1): 24-34.e10, 2019 07 24.
Artigo em Inglês | MEDLINE | ID: mdl-31344359

RESUMO

We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)-mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations-comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the 'legacy' GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as 'harmonized' by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve.


Assuntos
Genoma/genética , MicroRNAs/genética , Neoplasias/genética , Software , Estudos Controlados Antes e Depois , Conjuntos de Dados como Assunto , Perfilação da Expressão Gênica , Genoma Humano , Genômica , Troca de Informação em Saúde , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Anotação de Sequência Molecular , Reprodutibilidade dos Testes
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA